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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning

AWS CDK provides the ability to manage changes for the complete solution. The automated pipeline includes steps for out-of-the-box model storage and metric tracking. About the Authors Kiran Kumar Ballari is a Principal Solutions Architect at Amazon Web Services (AWS).

Analytics 129
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Run your local machine learning code as Amazon SageMaker Training jobs with minimal code changes

AWS Machine Learning

We include an example of how to use the decorator function and the associated settings later in this post. In the following example code, we run a simple divide function as a SageMaker Training job: import boto3 import sagemaker from sagemaker.remote_function import remote sm_session = sagemaker.Session(boto_session=boto3.session.Session(region_name="us-west-2"))

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Query training results: This step calls the Lambda function to fetch the metrics of the completed training job from the earlier model training step. RMSE threshold: This step verifies the trained model metric (RMSE) against a defined threshold to decide whether to proceed towards endpoint deployment or reject this model.

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Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models

AWS Machine Learning

Bosch is a multinational corporation with entities operating in multiple sectors, including automotive, industrial solutions, and consumer goods. These metrics provide business planning insights at different levels of aggregation and enable data-driven decision-making. Evaluation metrics. Evaluation.

APIs 96
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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

The context will be coming from your RAG solutions like Amazon Bedrock Knowledgebases. For this example, we take a sample context and add to demo the concept: input_output_demarkation_key = "nn### Response:n" question = "Tell me what was the improved inflow value of cash?" See Amazon Bedrock Recipes and GitHub for more examples.

APIs 141
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HCLTech’s AWS powered AutoWise Companion: A seamless experience for informed automotive buyer decisions with data-driven design

AWS Machine Learning

Each model has different features, price points, and performance metrics, making it difficult to make a confident choice that fits their needs and budget. By incorporating guardrails, the solution proactively steers users away from potential risks or errors, promoting better outcomes and adherence to established standards.